Metacognitive insight into cognitive performance in Huntington’s disease gene carriers

Objectives Insight is an important predictor of quality of life in Huntington’s disease and other neurodegenerative conditions. However, estimating insight with traditional methods such as questionnaires is challenging and subjected to limitations. This cross-sectional study experimentally quantified metacognitive insight into cognitive performance in Huntington’s disease gene carriers. Methods We dissociated perceptual decision-making performance and metacognitive insight into performance in healthy controls (n=29), premanifest (n=19) and early-manifest (n=10) Huntington’s disease gene carriers. Insight was operationalised as the degree to which a participant’s confidence in their performance was informative of their actual performance (metacognitive efficiency) and estimated using a computational model (HMeta-d’). Results We found that premanifest and early-manifest Huntington’s disease gene carriers were impaired in making perceptual decisions compared with controls. Gene carriers required more evidence in favour of the correct choice to achieve similar performance and perceptual impairments were increased in those with manifest disease. Surprisingly, despite marked perceptual impairments, Huntington’s disease gene carriers retained metacognitive insight into their perceptual performance. This was the case after controlling for confounding variables and regardless of disease stage. Conclusion We report for the first time a dissociation between impaired cognition and intact metacognition (trial-by-trial insight) in the early stages of a neurodegenerative disease. This unexpected finding contrasts with the prevailing assumption that cognitive deficits are associated with impaired insight. Future studies should investigate how intact metacognitive insight could be used by some early Huntington’s disease gene carriers to positively impact their quality of life.

Supplementary Figure 1. The hierarchical drift diffusion model was used to understand a decision between two choices as a noisy process of evidence accumulation through time. It calculates four latent parameters: drift rate (v; also called evidence accumulation), threshold (a), bias (z) and non-decision time (t). Information accumulates towards one of two boundaries (separated by a) with an average drift rate (v). Bias indicates the starting point likelihood towards one boundary. The flat line which precedes evidence accumulation (t) represents non-decision time, which includes time to encode stimuli and execute a motor response. This schematic shows three representative examples and not real data. Figure adapted from [4].

Model comparison and validation
The best-fitting model to our data was determined by implementing several regression models within HDDM, in which responses were coded as correct and incorrect choices and drift rate (v) was modulated by stimulus strength on every trial (Supplementary Table 1). This is because we manipulated trial-by-trial stimulus strength and this is known to directly influence accumulation of evidence [3,5]. The bias parameter was not included because by design, the task controlled the likelihood of a decision being correct or incorrect. To test our hypothesis that HD gene carriers would show impairments in perceptual decision-making, we tested for a decoupling between evidence accumulation rate and the evidence presented to them. To do so, Z-scores of stimulus strength were calculated within subjects. Therefore, each participant had their own Z-scores, reflecting the distribution of evidence (stimulus strength) they were presented with across the experiment. This allowed us to determine the relationship between drift rate in individuals carrying the HD gene, without the confounding influence of absolute differences in stimulus strength, which we explicitly manipulated in order to control perceptual task performance (Δ dots; Figure 2B). Prior to analysing the posterior distributions of the best fitting model, we confirmed the model's reproducibility. We ran four, independent models in parallel to confirm the convergence of the resulting parameters using Rhat statistic. Rhat (or Gelman-Rubin) statistic is the ratio of the variance of each parameter when pooled together across the four models, to the within model variance. Therefore, Rhat quantifies the extent to which separate models reach different conclusions [6]. Model parameters demonstrated excellent convergence for all estimated parameters (mean: 1.00003, range: 0.99998 -1.00015; Supplementary Table 2). Satisfied with this, we combined the chains of the four models and analysed the posterior distributions of the combined best-fitting model, which increased the sample size for the parameter estimates (80,000 chains, initial 4000 discarded). Of note, a model with a group term for non-decision time was a poorer fit to our data, which suggests that non-decision time did not differ between the groups. A further validation of the model is that, based on the parameters, we are able to reproduce the behaviour of our participants. To confirm this, we performed a posterior predictive check in which we simulated response time distributions generated from the posterior distributions of the model parameters and compared them with observed response times. HDDM simulates 500 response time distributions for each participant independently and quantiles are the mean across all simulations. Taking all participants together, the model reproduced response times accurately. This was also the case for each group, and for both correct and incorrect responses (Supplementary Figure 2). For example, the model reproduces the (non-significant) trend toward faster response times with HD (See main text, Figure 2).

Posterior distribution analysis
To assess if meaningful differences in parameter estimates existed between the groups, we compared the posterior distributions of each group directly and calculated the probability that the difference between the group distributions was in the opposite direction. This is similar to a one-tailed t-test (we calculated the probability, P, that the distribution with the greater mean was in fact smaller) and considered probability (P) < 0.025 (one-tailed) as statistically significant.
At the group level (considering all trials equally), there was a significant increase in the drift rate parameter in the premanifest group (M = 0.614, SD = 0.021) compared with the control group (M = 0.561, SD = 0.016; P = 0.022). Drift rate in the early-manifest group did not significantly differ from the control group (M = 0.595, SD = 0.03, P = 0.16) or the premanifest group (P = 0.31; Supplementary Figure 3A). However, such overall group differences do not take into account differences in stimulus strength (Δ dots) between the groups which we explicitly manipulated based on participant's accuracy (see main text, Figure 2). Consistent with our hypothesis, we found that the interaction effect of group*Zstimulus strength on drift rate revealed significant differences between both HD groups and the controls. groups, healthy controls responded to relatively stronger evidence in favour of the correct decision by accumulating evidence more quickly. There was no difference between the premanifest and the early-manifest groups (P = 0.34; Supplementary Figure 3B), implying that this deficit emerges early in HD and is stable between disease stages.
Comparing the decision threshold parameter, we found further significant differences between the groups. Patients with early-manifest HD adopted the lowest threshold (M = 1.69, SD = 0.018), which was significantly reduced compared to the premanifest genecarriers (M = 1.89, SD = 0.016, P < 0.001) and the control group (M = 1.99, SD = 0.014, P < 0.001). The threshold adopted by the premanifest group was also significantly reduced compared to the control group (P < 0.001; Supplementary Figure 3C). In summary, decision thresholds were consistently narrowed with increased disease status.